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Found 18 papers, 7 papers with code
Polynomial Regression Network for Variable-Number Lane Detection
Most of previous methods utilize semantic segmentation to identify the regions of traffic lanes in an image, and then adopt some curve-fitting method to reconstruct the lanes.
Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach
It comprises two core components: a snippet clustering component that groups the snippets into multiple latent clusters and a cluster classification component that further classifies the cluster as foreground or background.
How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model
In situations where unlimited annotation time was available, precise annotations still lead to the highest segmentation model performance.
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
ABM's strength lies in its bottom-up methodology, illuminating emergent phenomena by modeling the behaviors of individual components of a system.
Exploiting Low-confidence Pseudo-labels for Source-free Object Detection
The LPU module consists of Proposal Soft Training (PST) and Local Spatial Contrastive Learning (LSCL).
Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation
Shape learning, or the ability to leverage shape information, could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes.
Segment Anything Model for Medical Image Analysis: an Experimental Study
We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others.
Mutilmodal Feature Extraction and Attention-based Fusion for Emotion Estimation in Videos
The continuous improvement of human-computer interaction technology makes it possible to compute emotions.
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation
To this end, we first regard the classifier weights of the source model as class prototypes to compute class relationship, and then propose a novel probability-based similarity between target-domain samples by embedding the source-domain class relationship, resulting in Class Relationship embedded Similarity (CRS).
Preparing the Future for Continual Semantic Segmentation
In this study, we focus on Continual Semantic Segmentation (CSS) and present a novel approach to tackle the issue of existing methods struggling to learn new classes.
Towards Effective Instance Discrimination Contrastive Loss for Unsupervised Domain Adaptation
We further propose a target-dominated cross-domain mixup that can incorporate accurate semantic information from the source domain.
Enhancing Sequential Recommendation with Graph Contrastive Learning
Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data.
Who will stay? Using Deep Learning to predict engagement of citizen scientists
Using data from the annotation activity of citizen scientists in a Swedish marine project, we constructed Deep Neural Network models to predict forthcoming engagement.
Low-confidence Samples Matter for Domain Adaptation
Representing the target data structure in such a way would overlook the huge low-confidence samples, resulting in sub-optimal transferability that is biased towards the samples similar to the source domain.
5th Place Solution for VSPW 2021 Challenge
In this article, we introduce the solution we used in the VSPW 2021 Challenge.
Probabilistic Contrastive Learning for Domain Adaptation
However, it is undesirably observed that the standard contrastive paradigm (features+$\ell_{2}$ normalization) only brings little help for domain adaptation.
RPN Prototype Alignment for Domain Adaptive Object Detector
It essentially cooperates the learning of RPN prototypes and features to align the source and target RPN features.
Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments
Often, these missions start with building a map for the environment via pure exploration and subsequently using (i. e. exploiting) the generated map for downstream navigation tasks.
